Characteristics of deformation and damage of surrounding rock along the top roadway in the working face of an isolated island and its evolution law.

This study investigates the deformation and damage characteristics of the surrounding rock along the top return mining roadway of an isolated island working face at different stages and reveals its damage mechanism and evolution law. Utilizing a mine in Yangquan City, Shanxi Province, China, as the engineering background, this research employs FLAC 3D numerical simulation and on-site measurements. The findings suggest that the evolution of the plastic zone along the top roadway of the 15,106 island face is largely similar during both the excavation and mining periods. The plastic zones on either side of the roadway are expanding asymmetrically and gradually merging into the plastic zone of the coal pillar. In the destructive stage, the sub-gangs of the roadway are penetrated, indicating the progression into the plastic zone. The investigation points to extensive damage on the larger side of the roadway, the development of fissures, and the significant depth of damage as primary causes of roadway deformation. Moreover, the extent of the plastic zones on both sides of the roadway correlates positively with their relative distance. Continuous monitoring reveals an ongoing increase in roadway displacement, consistent with general observations in coal mining. The results provide valuable insights for optimizing support structures in similar mining environments.

Situation analysis and blood transfusion strategy of Lewis antibodies in Hunan Province.

BACKGROUND: Detection rate, serological characteristics, and clinical data of patients with Lewis blood group antibodies in Hunan Province were analyzed through retrospective analysis. This was undertaken in order to optimize the detection methods and blood transfusion strategies of these patients. METHODS: Blood typing, antibody screening, and cross-matching were performed by microcolumn gel, and Lewis antigen was detected by immediate spin test, antibody identification of positive and negative ABO samples, positive antibody screening, and cross-blood mismatch samples. Antibodies were identified by immediate spin test and microcolumn gel antiglobulin method, and the clinical data of the patients with Lewis antibody characteristics were analyzed. RESULTS: A total of 74 samples (15.91%) with Lewis antibodies were detected from 465 positive samples; cases were distributed in different cities of Hunan Province, with Changsha city being the most frequent (28%) one, with mostly non-O (66), anti-Lea (31; 41.89%), anti-Lea+anti-Leb (23; 31.08%), anti-Leb (5; 6.76%), anti-LebH and anti-Lea+anti-LebH (1+4; 6.76%), and antibody types immunoglobulin M (IgM) (51; 68.92%), immunoglobulin G (8; 10.81%), and IgG+IgM (4; 5.41%) cases. Patients included more females (67.57%) than males. The detection rate of gynecological diseases and patients with solid tumors was highest (44.59%). In all cases, the Lewis blood group was Le (a-b-); none of the 15 transfusion patients had hemolytic transfusion reaction. CONCLUSION: A variety of experimental methods must be adopted simultaneously to determine specificity and prevent the leakage of Lewis antibodies. The infusion of red blood cells matching with antiglobulin media at 37°C was recommended to ensure safe transfusion for recipients with Lewis antibodies.

Situation analysis and blood transfusion strategy of Lewis antibodies in Hunan Province

Background: Detection rate, serological characteristics, and clinical data of patients with Lewis blood group antibodies in Hunan Province were analyzed through retrospective analysis. This was undertaken in order to optimize the detection methods and blood transfusion strategies of these patients. Methods: Blood typing, antibody screening, and cross-matching were performed by microcolumn gel, and Lewis antigen was detected by immediate spin test, antibody identification of positive and negative ABO samples, positive antibody screening, and cross-blood mismatch samples. Antibodies were identified by immediate spin test and microcolumn gel antiglobulin method, and the clinical data of the patients with Lewis antibody characteristics were analyzed. Results: A total of 74 samples (15.91%) with Lewis antibodies were detected from 465 positive samples; cases were distributed in different cities of Hunan Province, with Changsha city being the most frequent (28%) one, with mostly non-O (66), anti-Lea (31; 41.89%), anti-Lea+anti-Leb (23; 31.08%), anti-Leb (5; 6.76%), anti-LebH and anti-Lea+anti-LebH (1+4; 6.76%), and antibody types immunoglobulin M (IgM) (51; 68.92%), immunoglobulin G (8; 10.81%), and IgG+IgM (4; 5.41%) cases. Patients included more females (67.57%) than males. The detection rate of gynecological diseases and patients with solid tumors was highest (44.59%). In all cases, the Lewis blood group was Le (a-b-); none of the 15 transfusion patients had hemolytic transfusion reaction. Conclusion: A variety of experimental methods must be adopted simultaneously to determine specificity and prevent the leakage of Lewis antibodies. The infusion of red blood cells matching with antiglobulin media at 37°C was recommended to ensure safe transfusion for recipients with Lewis antibodies (AU)

Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework.

Federated learning is a privacy-preserving distributed learning paradigm where multiple devices collaboratively train a model, which is applicable to edge computing environments. However, the non-IID data distributed in multiple devices degrades the performance of the federated model due to severe weight divergence. This paper presents a clustered federated learning framework named cFedFN for visual classification tasks in order to reduce the degradation. Especially, this framework introduces the computation of feature norm vectors in the local training process and divides the devices into multiple groups by the similarities of the data distributions to reduce the weight divergences for better performance. As a result, this framework gains better performance on non-IID data without leakage of the private raw data. Experiments on various visual classification datasets demonstrate the superiority of this framework over the state-of-the-art clustered federated learning frameworks.

Balanced Federated Semisupervised Learning With Fairness-Aware Pseudo-Labeling.

Federated semisupervised learning (FSSL) aims to train models with both labeled and unlabeled data in the federated settings, enabling performance improvement and easier deployment in realistic scenarios. However, the nonindependently identical distributed data in clients leads to imbalanced model training due to the unfair learning effects on different classes. As a result, the federated model exhibits inconsistent performance on not only different classes, but also different clients. This article presents a balanced FSSL method with the fairness-aware pseudo-labeling (FAPL) strategy to tackle the fairness issue. Specifically, this strategy globally balances the total number of unlabeled data samples which is capable to participate in model training. Then, the global numerical restrictions are further decomposed into personalized local restrictions for each client to assist the local pseudo-labeling. Consequently, this method derives a more fair federated model for all clients and gains better performance. Experiments on image classification datasets demonstrate the superiority of the proposed method over the state-of-the-art FSSL methods.

Self-powered droplet manipulation system for microfluidics based on triboelectric nanogenerator harvesting rotary energy.

Microfluidic technology, as a method for manipulating tiny fluids, has the advantages of low sample consumption, fast reaction, and no cross-contamination. In a microfluidic system, accurate manipulation of droplets is a crucial technology that has been widely investigated. In this work, a self-powered droplet manipulation system (SDMS) is proposed to realize various droplet operations, including moving, splitting, merging, mixing, transporting chemicals and reacting. The SDMS is mainly composed of a triboelectric nanogenerator (TENG), an electric brush, and a microfluidic device. The TENG serves as a high-voltage source to power the system. Using different electric brushes and microfluidic devices, different manipulations of droplets can be achieved. Moreover, by experiments and simulations, the influence of the electrode width, the electrode gap and the central angle of one electrode on the performance of SDMS is analyzed in detail. Firstly, by using electrowetting-on-dielectric (EWOD) technology, SDMS can accurately control droplets for long-distance linear movement and simultaneously control multiple droplets to move in a circular electrode track consisting of 40 electrodes. SDMS can also manipulate two droplets of different components to merge and react. In addition, using dielectrophoresis (DEP) technology, SDMS can separate droplets with maximum volumes of 400 µL and reduce the time of the complete mixing of two droplets with different components by 6.3 times compared with the passive mixing method. Finally, the demonstration shows that a droplet can be manipulated by hand power for chemical delivery and chemical reactions on a circular electrode track without an external power source, which proves the applicability of SDMS as an open-surface microfluidic device. Therefore, the self-powered droplet manipulation system proposed in this work may have great application in the fields of drug delivery, micro chemical reactions, and biological microanalysis.

High-quality blind defocus deblurring of multispectral images with optics and gradient prior.

This paper presents a blind defocus deblurring method that produces high-quality deblurred multispectral images. The high quality is achieved by two means: i) more accurate kernel estimation based on the optics prior by simulating the simple lens imaging, and ii) the gradient-based inter-channel correlation with the reference image generated by the content-adaptive combination of adjacent channels for restoring the latent sharp image. As a result, our method gains the prominence on both effectiveness and efficiency in deblurring defocus multispectral images with very good restoration on the obscure details. The experiments on some multispectral image datasets demonstrate the advantages of our method over state-of-the-art deblurring methods.

A Direction Self-Tuning Two-Dimensional Piezoelectric Vibration Energy Harvester.

Most work from the last decade on the piezoelectric vibration energy harvester (PVEHs) focuses on how to increase its frequency bandwidth but ignores the effect of vibration direction on the output performance of the harvester. However, both the frequency and the direction of the vibration in a real environment are time-variant. Therefore, improving the capability of PVEH to harvest multi-directional vibration energy is also important. This work presents a direction self-tuning two-dimensional (2D) PVEH, which consists of a spring-mass system and a direction self-tuning structure. The spring-mass system is sensitive to external vibration, and the direction self-tuning structure can automatically adjust its plane perpendicular to the direction of the external excitation driven by an external torque. The direction self-tuning mechanism is first theoretically analyzed. The experimental results show that this direction self-tuning PVEH can efficiently scavenge vibration energy in the 2D plane, and its output performance is unaffected by vibration direction and is very stable. Meanwhile, the effect of the initial deflection angle and the vibration acceleration on the direction self-tuning time of the PVEH is investigated. The direction self-tuning mechanism can also be used in other PVEHs with different energy conversion methods for harvesting multi-direction vibration energy.